{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 306,
   "id": "7bce7a2a-1a18-47ca-8d79-49ce59323d0c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.model_selection import train_test_split, cross_val_score\n",
    "from sklearn.linear_model import LinearRegression, LassoCV, RidgeCV, ElasticNetCV\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.metrics import r2_score, mean_squared_error, mean_absolute_error\n",
    "import joblib\n",
    "import warnings\n",
    "\n",
    "# 设置警告和可视化\n",
    "warnings.filterwarnings('ignore')\n",
    "plt.rcParams['font.family'] = 'SimHei'  # 全局字体设置\n",
    "plt.rcParams['axes.unicode_minus'] = False  # 正常显示负号\n",
    "sns.set_style(\"whitegrid\", {'font.sans-serif': 'SimHei'})  # 设置Seaborn字体"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7301d783-04cf-4424-b16a-5bf9892fe20f",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "# 数据导入与查看"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "37dbd60e-871a-403a-a905-6c197fe15ed6",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data = pd.read_csv('Train.csv')\n",
    "test_data = pd.read_csv('Test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "1806f8ee-610c-462a-bb31-58b67d725f2c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "训练集前5行：\n",
      "  POSTED_BY  UNDER_CONSTRUCTION  RERA  BHK_NO. BHK_OR_RK    SQUARE_FT  \\\n",
      "0     Owner                   0     0        2       BHK  1300.236407   \n",
      "1    Dealer                   0     0        2       BHK  1275.000000   \n",
      "2     Owner                   0     0        2       BHK   933.159722   \n",
      "3     Owner                   0     1        2       BHK   929.921143   \n",
      "4    Dealer                   1     0        2       BHK   999.009247   \n",
      "\n",
      "   READY_TO_MOVE  RESALE                      ADDRESS  LONGITUDE   LATITUDE  \\\n",
      "0              1       1        Ksfc Layout,Bangalore  12.969910  77.597960   \n",
      "1              1       1    Vishweshwara Nagar,Mysore  12.274538  76.644605   \n",
      "2              1       1             Jigani,Bangalore  12.778033  77.632191   \n",
      "3              1       1  Sector-1 Vaishali,Ghaziabad  28.642300  77.344500   \n",
      "4              0       1             New Town,Kolkata  22.592200  88.484911   \n",
      "\n",
      "   TARGET(PRICE_IN_LACS)  \n",
      "0                   55.0  \n",
      "1                   51.0  \n",
      "2                   43.0  \n",
      "3                   62.5  \n",
      "4                   60.5  \n",
      "\n",
      "测试集前5行：\n",
      "  POSTED_BY  UNDER_CONSTRUCTION  RERA  BHK_NO. BHK_OR_RK    SQUARE_FT  \\\n",
      "0     Owner                   0     0        1       BHK   545.171340   \n",
      "1    Dealer                   1     1        2       BHK   800.000000   \n",
      "2    Dealer                   0     0        2       BHK  1257.096513   \n",
      "3    Dealer                   0     0        3       BHK  1400.329489   \n",
      "4     Owner                   0     0        1       BHK   430.477830   \n",
      "\n",
      "   READY_TO_MOVE  RESALE             ADDRESS  LONGITUDE   LATITUDE  \n",
      "0              1       1        Kamrej,Surat  21.262000  73.047700  \n",
      "1              0       0     Panvel,Lalitpur  18.966114  73.148278  \n",
      "2              1       1    New Town,Kolkata  22.592200  88.484911  \n",
      "3              1       1  Kalwar Road,Jaipur  26.988300  75.584600  \n",
      "4              1       1   Mai Mandir,Nadiad  22.700000  72.870000  \n"
     ]
    }
   ],
   "source": [
    "# 查看训练集的前几行（默认5行）\n",
    "print(\"训练集前5行：\")\n",
    "print(train_data.head())\n",
    "\n",
    "# 查看测试集的前几行（默认5行）\n",
    "print(\"\\n测试集前5行：\")\n",
    "print(test_data.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "bff0ff70-72fb-45b5-8c29-a86d4f8be06f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "训练集信息：\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 29451 entries, 0 to 29450\n",
      "Data columns (total 12 columns):\n",
      " #   Column                 Non-Null Count  Dtype  \n",
      "---  ------                 --------------  -----  \n",
      " 0   POSTED_BY              29451 non-null  object \n",
      " 1   UNDER_CONSTRUCTION     29451 non-null  int64  \n",
      " 2   RERA                   29451 non-null  int64  \n",
      " 3   BHK_NO.                29451 non-null  int64  \n",
      " 4   BHK_OR_RK              29451 non-null  object \n",
      " 5   SQUARE_FT              29451 non-null  float64\n",
      " 6   READY_TO_MOVE          29451 non-null  int64  \n",
      " 7   RESALE                 29451 non-null  int64  \n",
      " 8   ADDRESS                29451 non-null  object \n",
      " 9   LONGITUDE              29451 non-null  float64\n",
      " 10  LATITUDE               29451 non-null  float64\n",
      " 11  TARGET(PRICE_IN_LACS)  29451 non-null  float64\n",
      "dtypes: float64(4), int64(5), object(3)\n",
      "memory usage: 2.7+ MB\n",
      "None\n",
      "\n",
      "测试集信息：\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 68720 entries, 0 to 68719\n",
      "Data columns (total 11 columns):\n",
      " #   Column              Non-Null Count  Dtype  \n",
      "---  ------              --------------  -----  \n",
      " 0   POSTED_BY           68720 non-null  object \n",
      " 1   UNDER_CONSTRUCTION  68720 non-null  int64  \n",
      " 2   RERA                68720 non-null  int64  \n",
      " 3   BHK_NO.             68720 non-null  int64  \n",
      " 4   BHK_OR_RK           68720 non-null  object \n",
      " 5   SQUARE_FT           68720 non-null  float64\n",
      " 6   READY_TO_MOVE       68720 non-null  int64  \n",
      " 7   RESALE              68720 non-null  int64  \n",
      " 8   ADDRESS             68720 non-null  object \n",
      " 9   LONGITUDE           68720 non-null  float64\n",
      " 10  LATITUDE            68720 non-null  float64\n",
      "dtypes: float64(3), int64(5), object(3)\n",
      "memory usage: 5.8+ MB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "# 查看数据集的基本信息（列名、数据类型、非空值数量等）\n",
    "print(\"\\n训练集信息：\")\n",
    "print(train_data.info())\n",
    "\n",
    "print(\"\\n测试集信息：\")\n",
    "print(test_data.info())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d216d30-704b-493b-8629-75a5a009a21f",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "# 数据预处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "3ef9f241-cc03-4300-bfd5-e472987dbf77",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== 训练集缺失值检查 ===\n",
      "每列缺失值数量：\n",
      "Series([], dtype: int64)\n",
      "训练集总缺失值数量：0\n",
      "\n",
      "=== 测试集缺失值检查 ===\n",
      "每列缺失值数量：\n",
      "Series([], dtype: int64)\n",
      "测试集总缺失值数量：0\n"
     ]
    }
   ],
   "source": [
    "# 1. 检查缺失值\n",
    "print(\"=== 训练集缺失值检查 ===\")\n",
    "missing_train = train_data.isnull().sum()\n",
    "print(\"每列缺失值数量：\")\n",
    "print(missing_train[missing_train > 0])  # 只显示有缺失值的列\n",
    "print(f\"训练集总缺失值数量：{missing_train.sum()}\")\n",
    "\n",
    "print(\"\\n=== 测试集缺失值检查 ===\")\n",
    "missing_test = test_data.isnull().sum()\n",
    "print(\"每列缺失值数量：\")\n",
    "print(missing_test[missing_test > 0])  # 只显示有缺失值的列\n",
    "print(f\"测试集总缺失值数量：{missing_test.sum()}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "e87ca6fa-1823-4c45-8007-0508a6496b8a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "=== 训练集重复值检查 ===\n",
      "训练集重复行数：401\n",
      "\n",
      "=== 测试集重复值检查 ===\n",
      "测试集重复行数：2470\n"
     ]
    }
   ],
   "source": [
    "# 2. 检查重复值\n",
    "print(\"\\n=== 训练集重复值检查 ===\")\n",
    "duplicate_train = train_data.duplicated().sum()\n",
    "print(f\"训练集重复行数：{duplicate_train}\")\n",
    "\n",
    "print(\"\\n=== 测试集重复值检查 ===\")\n",
    "duplicate_test = test_data.duplicated().sum()\n",
    "print(f\"测试集重复行数：{duplicate_test}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "80c2ff9e-1d85-437d-a033-aafb077d16fe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== 训练集重复值处理 ===\n",
      "训练集原始行数：29451，去除重复后行数：29050\n",
      "\n",
      "=== 测试集重复值处理 ===\n",
      "测试集原始行数：68720，去除重复后行数：66250\n"
     ]
    }
   ],
   "source": [
    "# 去除重复值\n",
    "print(\"=== 训练集重复值处理 ===\")\n",
    "initial_train_rows = len(train_data)\n",
    "train_data = train_data.drop_duplicates()  # 删除重复行\n",
    "print(f\"训练集原始行数：{initial_train_rows}，去除重复后行数：{len(train_data)}\")\n",
    "\n",
    "print(\"\\n=== 测试集重复值处理 ===\")\n",
    "initial_test_rows = len(test_data)\n",
    "test_data = test_data.drop_duplicates()  # 删除重复行\n",
    "print(f\"测试集原始行数：{initial_test_rows}，去除重复后行数：{len(test_data)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "0236a5a1-db9a-46eb-95dd-227308de732c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "处理后的训练集已保存为 'train_cleaned.csv'\n",
      "处理后的测试集已保存为 'test_cleaned.csv'\n"
     ]
    }
   ],
   "source": [
    "train_data.to_csv('train_cleaned.csv', index=False)\n",
    "test_data.to_csv('test_cleaned.csv', index=False)\n",
    "print(\"\\n处理后的训练集已保存为 'train_cleaned.csv'\")\n",
    "print(\"处理后的测试集已保存为 'test_cleaned.csv'\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ccbee60-5f22-489e-98ef-9a9f0d71a724",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "# 数据分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "id": "105aeadc-a7d6-4475-976d-6a28b6b694a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data = pd.read_csv('train_cleaned.csv')\n",
    "test_data = pd.read_csv('test_cleaned.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "id": "51e88598-f07c-4ac4-b1fb-d721dab9143d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "=== 分类列分布 ===\n",
      "\n",
      "列 'POSTED_BY' 的值分布：\n",
      "POSTED_BY\n",
      "Dealer     17961\n",
      "Owner      10470\n",
      "Builder      619\n",
      "Name: count, dtype: int64\n",
      "\n",
      "列 'BHK_OR_RK' 的值分布：\n",
      "BHK_OR_RK\n",
      "BHK    29026\n",
      "RK        24\n",
      "Name: count, dtype: int64\n",
      "\n",
      "列 'ADDRESS' 的值分布：\n",
      "ADDRESS\n",
      "Zirakpur,Chandigarh                       474\n",
      "Whitefield,Bangalore                      222\n",
      "Raj Nagar Extension,Ghaziabad             212\n",
      "Sector-137 Noida,Noida                    136\n",
      "New Town,Kolkata                          129\n",
      "                                         ... \n",
      "haripur phutkuwan Rampur Road,Haldwani      1\n",
      "Kolshet,Lalitpur                            1\n",
      "Ambika Township,Jivarajpark,Rajkot          1\n",
      "Cheranalloor,Kochi                          1\n",
      "E3-108, Lake View Recidency,,Vapi           1\n",
      "Name: count, Length: 6899, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x1500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 2. 分类列分布及可视化\n",
    "print(\"\\n=== 分类列分布 ===\")\n",
    "categorical_cols = ['POSTED_BY', 'BHK_OR_RK', 'ADDRESS']\n",
    "plt.figure(figsize=(15, 5 * len(categorical_cols)))\n",
    "for i, col in enumerate(categorical_cols, 1):\n",
    "    print(f\"\\n列 '{col}' 的值分布：\")\n",
    "    print(train_data[col].value_counts())\n",
    "    # 可视化：柱状图\n",
    "    plt.subplot(len(categorical_cols), 1, i)\n",
    "    if col == 'ADDRESS':\n",
    "        # 限制 ADDRESS 显示前 10 个高频值\n",
    "        sns.countplot(y=col, data=train_data, order=train_data[col].value_counts().index[:10])\n",
    "        plt.title(f\"'{col}' 前 10 类分布\")\n",
    "    else:\n",
    "        sns.countplot(x=col, data=train_data)\n",
    "        plt.title(f\"'{col}' 分布\")\n",
    "    plt.xlabel('计数' if col != 'ADDRESS' else col)\n",
    "    plt.ylabel(col if col != 'ADDRESS' else '计数')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "id": "636841cd-785e-4df7-98c6-cd5f75377478",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "=== 二值列分布 ===\n",
      "\n",
      "列 'UNDER_CONSTRUCTION' 的值分布：\n",
      "UNDER_CONSTRUCTION\n",
      "0    23851\n",
      "1     5199\n",
      "Name: count, dtype: int64\n",
      "\n",
      "列 'RERA' 的值分布：\n",
      "RERA\n",
      "0    19874\n",
      "1     9176\n",
      "Name: count, dtype: int64\n",
      "\n",
      "列 'READY_TO_MOVE' 的值分布：\n",
      "READY_TO_MOVE\n",
      "1    23851\n",
      "0     5199\n",
      "Name: count, dtype: int64\n",
      "\n",
      "列 'RESALE' 的值分布：\n",
      "RESALE\n",
      "1    27027\n",
      "0     2023\n",
      "Name: count, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1200 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 3. 二值列分布及可视化\n",
    "print(\"\\n=== 二值列分布 ===\")\n",
    "binary_cols = ['UNDER_CONSTRUCTION', 'RERA', 'READY_TO_MOVE', 'RESALE']\n",
    "plt.figure(figsize=(12, 3 * len(binary_cols)))\n",
    "for i, col in enumerate(binary_cols, 1):\n",
    "    print(f\"\\n列 '{col}' 的值分布：\")\n",
    "    print(train_data[col].value_counts())\n",
    "    # 可视化：柱状图\n",
    "    plt.subplot(len(binary_cols), 1, i)\n",
    "    sns.countplot(x=col, data=train_data)\n",
    "    plt.title(f\"'{col}' 分布\")\n",
    "    plt.xlabel(col)\n",
    "    plt.ylabel('计数')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "id": "b7036bd0-abc5-47da-879b-7bd0395eb4f9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "=== 数值列描述性统计 ===\n",
      "            BHK_NO.     SQUARE_FT     LONGITUDE      LATITUDE  \\\n",
      "count  29050.000000  2.905000e+04  29050.000000  29050.000000   \n",
      "mean       2.390809  2.003171e+04     21.270272     76.829585   \n",
      "std        0.880677  1.914411e+06      6.195973     10.567572   \n",
      "min        1.000000  3.000000e+00    -37.713008   -121.761248   \n",
      "25%        2.000000  9.000000e+02     18.452663     73.794800   \n",
      "50%        2.000000  1.172742e+03     20.631532     77.322873   \n",
      "75%        3.000000  1.550169e+03     26.886881     77.912934   \n",
      "max       20.000000  2.545455e+08     59.912884    152.962676   \n",
      "\n",
      "       TARGET(PRICE_IN_LACS)  \n",
      "count           29050.000000  \n",
      "mean              141.712677  \n",
      "std               650.055763  \n",
      "min                 0.250000  \n",
      "25%                38.000000  \n",
      "50%                61.750000  \n",
      "75%               100.000000  \n",
      "max             30000.000000  \n"
     ]
    }
   ],
   "source": [
    "# 4. 数值列描述性统计\n",
    "print(\"\\n=== 数值列描述性统计 ===\")\n",
    "numeric_cols = ['BHK_NO.', 'SQUARE_FT', 'LONGITUDE', 'LATITUDE', 'TARGET(PRICE_IN_LACS)']\n",
    "print(train_data[numeric_cols].describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "id": "514a0c3b-9a9b-4296-af34-ff9f467704e3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "=== 房价分布分析 ===\n",
      "房价偏度：17.971976026158366\n",
      "房价峰度：472.65556921137147\n"
     ]
    }
   ],
   "source": [
    "# 5. 房价分布分析\n",
    "print(\"\\n=== 房价分布分析 ===\")\n",
    "print(f\"房价偏度：{train_data['TARGET(PRICE_IN_LACS)'].skew()}\")\n",
    "print(f\"房价峰度：{train_data['TARGET(PRICE_IN_LACS)'].kurtosis()}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "id": "60a9e9be-a0bc-44fc-9476-1ae74844c092",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 房价分布直方图\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.histplot(train_data['TARGET(PRICE_IN_LACS)'], kde=True)\n",
    "plt.title('房价分布 (TARGET(PRICE_IN_LACS))')\n",
    "plt.xlabel('房价 (十万卢比)')\n",
    "plt.ylabel('频率')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "id": "919677e4-d487-4b23-af6d-92fc6796a6e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "correlation = train_data[numeric_cols].corr()\n",
    "# 相关性热图\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.heatmap(correlation, annot=True, cmap='coolwarm', center=0)\n",
    "plt.title('数值列相关性热图')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9182fde-8c05-41d5-8aa2-cfb746975fc1",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "# 特征工程"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "id": "451bf95f-ce11-4006-8d87-beba7936b14e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "=== 训练集 特征工程 ===\n",
      "已创建特征：log_SQUARE_FT（面积对数变换）\n",
      "已创建特征：log_TARGET_PRICE（房价对数变换）\n",
      "已创建特征：SQUARE_FT_per_BHK（每卧室面积）\n",
      "已创建特征：CITY（从 ADDRESS 提取城市）\n",
      "已编码特征：BHK_OR_RK 转为二分类编码（BHK=1, RK=0）\n",
      "\n",
      "=== 测试集 特征工程 ===\n",
      "已创建特征：log_SQUARE_FT（面积对数变换）\n",
      "已创建特征：SQUARE_FT_per_BHK（每卧室面积）\n",
      "已创建特征：CITY（从 ADDRESS 提取城市）\n",
      "已编码特征：BHK_OR_RK 转为二分类编码（BHK=1, RK=0）\n"
     ]
    }
   ],
   "source": [
    "#  特征工程\n",
    "def feature_engineering(df, dataset_name):\n",
    "    print(f\"\\n=== {dataset_name} 特征工程 ===\")\n",
    "    \n",
    "    # 对数变换（处理右偏分布）\n",
    "    df['log_SQUARE_FT'] = np.log1p(df['SQUARE_FT'])\n",
    "    print(\"已创建特征：log_SQUARE_FT（面积对数变换）\")\n",
    "    \n",
    "    if 'TARGET(PRICE_IN_LACS)' in df.columns:\n",
    "        df['log_TARGET_PRICE'] = np.log1p(df['TARGET(PRICE_IN_LACS)'])\n",
    "        print(\"已创建特征：log_TARGET_PRICE（房价对数变换）\")\n",
    "    \n",
    "    # 交互特征：每卧室面积\n",
    "    df['SQUARE_FT_per_BHK'] = df['SQUARE_FT'] / df['BHK_NO.']\n",
    "    print(\"已创建特征：SQUARE_FT_per_BHK（每卧室面积）\")\n",
    "    \n",
    "    # 从 ADDRESS 提取城市（假设 ADDRESS 格式为 \"地区,城市\"）\n",
    "    df['CITY'] = df['ADDRESS'].apply(lambda x: x.split(',')[-1].strip() if ',' in x else x)\n",
    "    print(\"已创建特征：CITY（从 ADDRESS 提取城市）\")\n",
    "\n",
    "    # 将 BHK_OR_RK 转为二分类编码（BHK=1, RK=0）\n",
    "    df['BHK_OR_RK'] = df['BHK_OR_RK'].map({'BHK': 1, 'RK': 0})\n",
    "    print(\"已编码特征：BHK_OR_RK 转为二分类编码（BHK=1, RK=0）\")\n",
    "    \n",
    "    return df\n",
    "\n",
    "train_data = feature_engineering(train_data, \"训练集\")\n",
    "test_data = feature_engineering(test_data, \"测试集\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "id": "f1bf4b19-c62e-4a37-8e77-bf8a8ea9b8ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "numeric_cols = ['BHK_NO.', 'SQUARE_FT', 'LONGITUDE', 'LATITUDE', 'log_SQUARE_FT', 'SQUARE_FT_per_BHK']\n",
    "categorical_cols = ['POSTED_BY', 'CITY'] \n",
    "binary_cols = ['UNDER_CONSTRUCTION', 'RERA', 'READY_TO_MOVE', 'RESALE', 'BHK_OR_RK']\n",
    "\n",
    "preprocessor = ColumnTransformer(\n",
    "    transformers=[\n",
    "        ('num', StandardScaler(), numeric_cols),  # 标准化数值列\n",
    "        ('cat', OneHotEncoder(handle_unknown='ignore', sparse_output=False), categorical_cols),  # 独热编码分类列\n",
    "        ('bin', 'passthrough', binary_cols)  # 保留二值列\n",
    "    ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "id": "d30b8250-7f75-48ad-8869-128c9cbd5a10",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 定义模型参数\n",
    "alphas = np.logspace(-4, 4, 200)\n",
    "l1_ratios = [0.1, 0.3, 0.5, 0.7, 0.9]\n",
    "\n",
    "# 定义模型管道\n",
    "models = {\n",
    "    \"LASSO回归\": Pipeline([\n",
    "        ('preprocessor', preprocessor),\n",
    "        ('regressor', LassoCV(alphas=alphas, cv=5, random_state=42))\n",
    "    ]),\n",
    "    \"岭回归\": Pipeline([\n",
    "        ('preprocessor', preprocessor),\n",
    "        ('regressor', RidgeCV(alphas=alphas, cv=5))\n",
    "    ]),\n",
    "    \"弹性网回归\": Pipeline([\n",
    "        ('preprocessor', preprocessor),\n",
    "        ('regressor', ElasticNetCV(alphas=alphas, l1_ratio=l1_ratios, cv=5, random_state=42))\n",
    "    ])\n",
    "}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "id": "2669c98f-123f-4466-b7cf-9cfd67f1b408",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "训练集分割：\n",
      "训练子集：23240 行，验证子集：5810 行\n"
     ]
    }
   ],
   "source": [
    "# 训练集分割\n",
    "X = train_data.drop(['TARGET(PRICE_IN_LACS)', 'log_TARGET_PRICE', 'ADDRESS'], axis=1)\n",
    "y = train_data['log_TARGET_PRICE']  # 使用对数变换后的房价\n",
    "X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "print(\"\\n训练集分割：\")\n",
    "print(f\"训练子集：{X_train.shape[0]} 行，验证子集：{X_val.shape[0]} 行\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab8a8e87-1ff6-494e-ad73-3f4f8e8285f6",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "# 模型训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "id": "b48b64c7-c256-4763-8892-0fb995690d0c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "训练模型：LASSO回归\n",
      "\n",
      "训练模型：岭回归\n",
      "\n",
      "训练模型：弹性网回归\n"
     ]
    }
   ],
   "source": [
    "# 训练并评估模型\n",
    "results = []\n",
    "for name, model in models.items():\n",
    "    print(f\"\\n训练模型：{name}\")\n",
    "    model.fit(X_train, y_train)\n",
    "    \n",
    "    # 获取最佳参数\n",
    "    params = {}\n",
    "    if hasattr(model.named_steps['regressor'], 'alpha_'):\n",
    "        params['alpha'] = model.named_steps['regressor'].alpha_\n",
    "    if hasattr(model.named_steps['regressor'], 'l1_ratio_'):\n",
    "        params['l1_ratio'] = model.named_steps['regressor'].l1_ratio_\n",
    "    \n",
    "    # 交叉验证评估（R²）\n",
    "    cv_scores = cross_val_score(model, X_train, y_train, cv=5, scoring='r2')\n",
    "    \n",
    "    # 验证集评估\n",
    "    y_val_pred = model.predict(X_val)\n",
    "    metrics = {\n",
    "        '模型': name,\n",
    "        'R²': r2_score(y_val, y_val_pred),\n",
    "        '调整R²': 1 - (1 - r2_score(y_val, y_val_pred)) * (len(y_val) - 1) / (len(y_val) - X_train.shape[1] - 1),\n",
    "        'MSE': mean_squared_error(y_val, y_val_pred),\n",
    "        'RMSE': np.sqrt(mean_squared_error(y_val, y_val_pred)),\n",
    "        'MAE': mean_absolute_error(y_val, y_val_pred),\n",
    "        '交叉验证R²均值': cv_scores.mean(),\n",
    "        '参数': params\n",
    "    }\n",
    "    results.append(metrics)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "id": "4013d208-1a7e-47f4-9a71-bbfef09ae6f7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "模型性能对比：\n",
      "               R²      调整R²       MSE      RMSE       MAE  交叉验证R²均值  \\\n",
      "模型                                                                    \n",
      "弹性网回归    0.737135  0.736545  0.206707  0.454650  0.308985  0.750492   \n",
      "LASSO回归  0.737132  0.736543  0.206709  0.454652  0.308963  0.749898   \n",
      "岭回归      0.735387  0.734794  0.208081  0.456159  0.310875  0.741667   \n",
      "\n",
      "                                                        参数  \n",
      "模型                                                          \n",
      "弹性网回归    {'alpha': 0.0002523539170434766, 'l1_ratio': 0.7}  \n",
      "LASSO回归                  {'alpha': 0.00019116440753857018}  \n",
      "岭回归                          {'alpha': 42.475715525369026}  \n"
     ]
    }
   ],
   "source": [
    "results_df = pd.DataFrame(results).set_index('模型')\n",
    "print(\"\\n模型性能对比：\")\n",
    "print(results_df.sort_values('R²', ascending=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "id": "27395768-6cf4-4a1e-a05e-5d714964b525",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
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      "Font 'default' does not have a glyph for '\\u2212' [U+2212], substituting with a dummy symbol.\n",
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      "Font 'default' does not have a glyph for '\\u2212' [U+2212], substituting with a dummy symbol.\n",
      "Font 'default' does not have a glyph for '\\u2212' [U+2212], substituting with a dummy symbol.\n"
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    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1400x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 可视化模型性能（双轴图）\n",
    "fig, ax1 = plt.subplots(figsize=(14, 7))\n",
    "\n",
    "# 左侧坐标轴（对数刻度误差指标）\n",
    "error_metrics_wide = results_df[['RMSE', 'MAE']].reset_index()\n",
    "sns.barplot(x='模型', y='RMSE', data=error_metrics_wide,\n",
    "            color='#1f77b4', alpha=0.7, label='RMSE', ax=ax1)\n",
    "sns.barplot(x='模型', y='MAE', data=error_metrics_wide,\n",
    "            color='#2ca02c', alpha=0.7, label='MAE', ax=ax1)\n",
    "\n",
    "# 设置对数坐标轴（根据实际数据调整范围）\n",
    "ax1.set_yscale('log')\n",
    "ax1.set_ylabel('误差值', fontsize=12)\n",
    "ax1.set_ylim(0.01, 10)  # 待运行后根据实际 RMSE/MAE 范围调整\n",
    "\n",
    "# 添加误差值标签\n",
    "for p in ax1.patches:\n",
    "    height = p.get_height()\n",
    "    ax1.annotate(f'{height:.3f}',\n",
    "                 (p.get_x() + p.get_width() / 2., height),\n",
    "                 ha='center', va='bottom',\n",
    "                 fontsize=9, color='black',\n",
    "                 xytext=(0, 4),\n",
    "                 textcoords='offset points')\n",
    "\n",
    "# 右侧坐标轴（R²指标）\n",
    "ax2 = ax1.twinx()\n",
    "r2_metrics = results_df[['R²', '调整R²']].reset_index()\n",
    "sns.lineplot(x='模型', y='R²', data=r2_metrics,\n",
    "             color='#d62728', marker='o', markersize=10,\n",
    "             linewidth=2.5, label='R²', ax=ax2)\n",
    "sns.lineplot(x='模型', y='调整R²', data=r2_metrics,\n",
    "             color='#ff7f0e', marker='s', markersize=8,\n",
    "             linewidth=2.5, linestyle='--', label='调整R²', ax=ax2)\n",
    "\n",
    "# 设置 R² 坐标轴范围（根据实际数据调整）\n",
    "ax2.set_ylim(0.734, 0.738)\n",
    "ax2.set_ylabel('R²值（线性刻度）', fontsize=12)\n",
    "ax2.grid(False)\n",
    "\n",
    "# 合并图例\n",
    "lines1, labels1 = ax1.get_legend_handles_labels()\n",
    "lines2, labels2 = ax2.get_legend_handles_labels()\n",
    "ax1.legend(lines1 + lines2, labels1 + labels2,\n",
    "           loc='upper center',\n",
    "           bbox_to_anchor=(0.5, -0.15),\n",
    "           ncol=4)\n",
    "\n",
    "# 标题与格式\n",
    "plt.title('模型综合性能对比（误差对数刻度 + R²精细刻度）', fontsize=14, pad=20)\n",
    "ax1.tick_params(axis='x', rotation=45)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "id": "e2977c67-4bdc-443b-8ad9-48a89153cc06",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "最佳模型：弹性网回归，验证集 RMSE：0.4547\n"
     ]
    }
   ],
   "source": [
    "best_model_name = results_df['RMSE'].idxmin()\n",
    "best_model = models[best_model_name]\n",
    "print(f\"\\n最佳模型：{best_model_name}，验证集 RMSE：{results_df.loc[best_model_name, 'RMSE']:.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "id": "16a23f16-63c9-4773-9db5-225ba063c08f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_val_pred = best_model.predict(X_val)\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.scatter(y_val, y_val_pred, alpha=0.5)\n",
    "plt.plot([y_val.min(), y_val.max()], [y_val.min(), y_val.max()], 'r--')\n",
    "plt.title(f'最佳模型 ({best_model_name}) 预测 vs 实际值（对数房价）')\n",
    "plt.xlabel('实际对数房价')\n",
    "plt.ylabel('预测对数房价')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "id": "0463a038-7f15-435f-aab7-fba857c893e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "coef = best_model.named_steps['regressor'].coef_\n",
    "feature_names = numeric_cols + list(best_model.named_steps['preprocessor'].named_transformers_['cat'].get_feature_names_out(categorical_cols)) + binary_cols\n",
    "feature_importance = pd.DataFrame({'Feature': feature_names, 'Coefficient': np.abs(coef)})\n",
    "feature_importance = feature_importance.sort_values(by='Coefficient', ascending=False).head(10)\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.barplot(x='Coefficient', y='Feature', data=feature_importance)\n",
    "plt.title(f'最佳模型 ({best_model_name}) 特征重要性（前 10）')\n",
    "plt.xlabel('系数绝对值')\n",
    "plt.ylabel('特征')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "437e2d5b-9707-424f-99b1-21813172a1fb",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "# 预测房价"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "id": "f9082d82-0944-4c88-9ba4-4416f004431c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "最佳模型已保存为 'best_model.joblib'\n"
     ]
    }
   ],
   "source": [
    "# 保存最佳模型\n",
    "joblib.dump(best_model, 'best_model.joblib')\n",
    "print(f\"最佳模型已保存为 'best_model.joblib'\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "id": "744c3bb1-f062-40ba-9a0f-f047b3f595d3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "已加载保存的最佳模型\n"
     ]
    }
   ],
   "source": [
    "# 加载最佳模型\n",
    "loaded_model = joblib.load('best_model.joblib')\n",
    "print(\"已加载保存的最佳模型\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "id": "ef786f17-f517-40a9-b0fd-d19f05621e97",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "测试集预测已保存为 'test_predictions.csv'\n"
     ]
    }
   ],
   "source": [
    "# 测试集预测并合并到 test_data\n",
    "y_test_pred = loaded_model.predict(test_data.drop(['TARGET(PRICE_IN_LACS)', 'log_TARGET_PRICE', 'ADDRESS'], axis=1, errors='ignore'))\n",
    "y_test_pred_original = np.expm1(y_test_pred)\n",
    "test_data['Predicted_Price_in_Lacs'] = y_test_pred_original.round(1)  # 保留一位小数\n",
    "# 将 BHK_OR_RK 转换回原始值\n",
    "test_data['BHK_OR_RK'] = test_data['BHK_OR_RK'].map({1: 'BHK', 0: 'RK'})\n",
    "# 删除自己创建的特征\n",
    "created_features = ['log_SQUARE_FT', 'SQUARE_FT_per_BHK', 'CITY']\n",
    "test_data = test_data.drop(columns=created_features, errors='ignore')\n",
    "test_data.to_csv('test_predictions.csv', index=False)\n",
    "print(\"\\n测试集预测已保存为 'test_predictions.csv'\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6af68381-9b95-4829-b6ff-e206ce0e0ea9",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
